import numpy as np
import pandas as pd
mydataset = {
'cars': ["BMW", "Volvo", "Ford"],
'passings': [3, 7, 2]
}
myvar = pd.DataFrame(mydataset)
print(myvar)
cars passings 0 BMW 3 1 Volvo 7 2 Ford 2
url = "Iris.csv"
df = pd.read_csv(url)
print(df)
Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm \
0 1 5.1 3.5 1.4 0.2
1 2 4.9 3.0 1.4 0.2
2 3 4.7 3.2 1.3 0.2
3 4 4.6 3.1 1.5 0.2
4 5 5.0 3.6 1.4 0.2
.. ... ... ... ... ...
145 146 6.7 3.0 5.2 2.3
146 147 6.3 2.5 5.0 1.9
147 148 6.5 3.0 5.2 2.0
148 149 6.2 3.4 5.4 2.3
149 150 5.9 3.0 5.1 1.8
Species
0 Iris-setosa
1 Iris-setosa
2 Iris-setosa
3 Iris-setosa
4 Iris-setosa
.. ...
145 Iris-virginica
146 Iris-virginica
147 Iris-virginica
148 Iris-virginica
149 Iris-virginica
[150 rows x 6 columns]
df.head(3)
| Id | SepalLengthCm | SepalWidthCm | PetalLengthCm | PetalWidthCm | Species | |
|---|---|---|---|---|---|---|
| 0 | 1 | 5.1 | 3.5 | 1.4 | 0.2 | Iris-setosa |
| 1 | 2 | 4.9 | 3.0 | 1.4 | 0.2 | Iris-setosa |
| 2 | 3 | 4.7 | 3.2 | 1.3 | 0.2 | Iris-setosa |
df.tail(6)
| Id | SepalLengthCm | SepalWidthCm | PetalLengthCm | PetalWidthCm | Species | |
|---|---|---|---|---|---|---|
| 144 | 145 | 6.7 | 3.3 | 5.7 | 2.5 | Iris-virginica |
| 145 | 146 | 6.7 | 3.0 | 5.2 | 2.3 | Iris-virginica |
| 146 | 147 | 6.3 | 2.5 | 5.0 | 1.9 | Iris-virginica |
| 147 | 148 | 6.5 | 3.0 | 5.2 | 2.0 | Iris-virginica |
| 148 | 149 | 6.2 | 3.4 | 5.4 | 2.3 | Iris-virginica |
| 149 | 150 | 5.9 | 3.0 | 5.1 | 1.8 | Iris-virginica |
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 150 entries, 0 to 149 Data columns (total 6 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Id 150 non-null int64 1 SepalLengthCm 150 non-null float64 2 SepalWidthCm 150 non-null float64 3 PetalLengthCm 150 non-null float64 4 PetalWidthCm 150 non-null float64 5 Species 150 non-null object dtypes: float64(4), int64(1), object(1) memory usage: 7.2+ KB
df.describe()
| Id | SepalLengthCm | SepalWidthCm | PetalLengthCm | PetalWidthCm | |
|---|---|---|---|---|---|
| count | 150.000000 | 150.000000 | 150.000000 | 150.000000 | 150.000000 |
| mean | 75.500000 | 5.843333 | 3.054000 | 3.758667 | 1.198667 |
| std | 43.445368 | 0.828066 | 0.433594 | 1.764420 | 0.763161 |
| min | 1.000000 | 4.300000 | 2.000000 | 1.000000 | 0.100000 |
| 25% | 38.250000 | 5.100000 | 2.800000 | 1.600000 | 0.300000 |
| 50% | 75.500000 | 5.800000 | 3.000000 | 4.350000 | 1.300000 |
| 75% | 112.750000 | 6.400000 | 3.300000 | 5.100000 | 1.800000 |
| max | 150.000000 | 7.900000 | 4.400000 | 6.900000 | 2.500000 |
df.columns
Index(['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',
'Species'],
dtype='object')
Sepla = df["SepalLengthCm"]
print(Sepla.head())
0 5.1 1 4.9 2 4.7 3 4.6 4 5.0 Name: SepalLengthCm, dtype: float64
url = "Iris.csv"
df = pd.read_csv(url,index_col="Id")
Sepla = df[["SepalLengthCm","SepalWidthCm","PetalLengthCm"]]
print(Sepla.head())
SepalLengthCm SepalWidthCm PetalLengthCm Id 1 5.1 3.5 1.4 2 4.9 3.0 1.4 3 4.7 3.2 1.3 4 4.6 3.1 1.5 5 5.0 3.6 1.4
gk = df.groupby("Species")
gk.first()
| SepalLengthCm | SepalWidthCm | PetalLengthCm | PetalWidthCm | |
|---|---|---|---|---|
| Species | ||||
| Iris-setosa | 5.1 | 3.5 | 1.4 | 0.2 |
| Iris-versicolor | 7.0 | 3.2 | 4.7 | 1.4 |
| Iris-virginica | 6.3 | 3.3 | 6.0 | 2.5 |
df.aggregate(['sum','min'])
| SepalLengthCm | SepalWidthCm | PetalLengthCm | PetalWidthCm | Species | |
|---|---|---|---|---|---|
| sum | 876.5 | 458.1 | 563.8 | 179.8 | Iris-setosaIris-setosaIris-setosaIris-setosaIr... |
| min | 4.3 | 2.0 | 1.0 | 0.1 | Iris-setosa |
df.iloc[[0, 1]]
| Id | SepalLengthCm | SepalWidthCm | PetalLengthCm | PetalWidthCm | Species | |
|---|---|---|---|---|---|---|
| 0 | 1 | 5.1 | 3.5 | 1.4 | 0.2 | Iris-setosa |
| 1 | 2 | 4.9 | 3.0 | 1.4 | 0.2 | Iris-setosa |
df.loc[[0,1,2,3,4,5,]]
| Id | SepalLengthCm | SepalWidthCm | PetalLengthCm | PetalWidthCm | Species | |
|---|---|---|---|---|---|---|
| 0 | 1 | 5.1 | 3.5 | 1.4 | 0.2 | Iris-setosa |
| 1 | 2 | 4.9 | 3.0 | 1.4 | 0.2 | Iris-setosa |
| 2 | 3 | 4.7 | 3.2 | 1.3 | 0.2 | Iris-setosa |
| 3 | 4 | 4.6 | 3.1 | 1.5 | 0.2 | Iris-setosa |
| 4 | 5 | 5.0 | 3.6 | 1.4 | 0.2 | Iris-setosa |
| 5 | 6 | 5.4 | 3.9 | 1.7 | 0.4 | Iris-setosa |
ind = pd.read_csv("IndiaCrime.csv")
ind.head(15)
| Area_Name | Year | Group_Name | Sub_Group_Name | Cases_Property_Recovered | Cases_Property_Stolen | Value_of_Property_Recovered | Value_of_Property_Stolen | |
|---|---|---|---|---|---|---|---|---|
| 0 | Andaman & Nicobar Islands | 2001 | Burglary - Property | 3. Burglary | 27 | 64 | 755858 | 1321961 |
| 1 | Andhra Pradesh | 2001 | Burglary - Property | 3. Burglary | 3321 | 7134 | 51483437 | 147019348 |
| 2 | Arunachal Pradesh | 2001 | Burglary - Property | 3. Burglary | 66 | 248 | 825115 | 4931904 |
| 3 | Assam | 2001 | Burglary - Property | 3. Burglary | 539 | 2423 | 3722850 | 21466955 |
| 4 | Bihar | 2001 | Burglary - Property | 3. Burglary | 367 | 3231 | 2327135 | 17023937 |
| 5 | Chandigarh | 2001 | Burglary - Property | 3. Burglary | 119 | 364 | 1804823 | 10217378 |
| 6 | Chhattisgarh | 2001 | Burglary - Property | 3. Burglary | 1169 | 4144 | 6518261 | 30457033 |
| 7 | Dadra & Nagar Haveli | 2001 | Burglary - Property | 3. Burglary | 10 | 34 | 247140 | 1333389 |
| 8 | Daman & Diu | 2001 | Burglary - Property | 3. Burglary | 7 | 43 | 479300 | 2084845 |
| 9 | Delhi | 2001 | Burglary - Property | 3. Burglary | 642 | 3029 | 39632177 | 150033824 |
| 10 | Goa | 2001 | Burglary - Property | 3. Burglary | 65 | 347 | 895875 | 9991574 |
| 11 | Gujarat | 2001 | Burglary - Property | 3. Burglary | 1124 | 4928 | 20821285 | 141650158 |
| 12 | Haryana | 2001 | Burglary - Property | 3. Burglary | 1257 | 3098 | 27369980 | 64576543 |
| 13 | Himachal Pradesh | 2001 | Burglary - Property | 3. Burglary | 93 | 812 | 2936881 | 17092490 |
| 14 | Jammu & Kashmir | 2001 | Burglary - Property | 3. Burglary | 161 | 1345 | 2905184 | 27222163 |
ind.tail(10)
| Area_Name | Year | Group_Name | Sub_Group_Name | Cases_Property_Recovered | Cases_Property_Stolen | Value_of_Property_Recovered | Value_of_Property_Stolen | |
|---|---|---|---|---|---|---|---|---|
| 2439 | Odisha | 2010 | Total Property | 7. Total Property Stolen & Recovered | 5690 | 12691 | 311033656 | 1116660883 |
| 2440 | Puducherry | 2010 | Total Property | 7. Total Property Stolen & Recovered | 325 | 625 | 18752582 | 30249484 |
| 2441 | Punjab | 2010 | Total Property | 7. Total Property Stolen & Recovered | 5885 | 9873 | 646232099 | 1056728815 |
| 2442 | Rajasthan | 2010 | Total Property | 7. Total Property Stolen & Recovered | 8551 | 28152 | 854388626 | 1395764020 |
| 2443 | Sikkim | 2010 | Total Property | 7. Total Property Stolen & Recovered | 38 | 134 | 1444190 | 9445146 |
| 2444 | Tamil Nadu | 2010 | Total Property | 7. Total Property Stolen & Recovered | 16125 | 21509 | 660311804 | 1317919190 |
| 2445 | Tripura | 2010 | Total Property | 7. Total Property Stolen & Recovered | 192 | 879 | 5666102 | 33032746 |
| 2446 | Uttar Pradesh | 2010 | Total Property | 7. Total Property Stolen & Recovered | 9130 | 35068 | 577591772 | 1442670414 |
| 2447 | Uttarakhand | 2010 | Total Property | 7. Total Property Stolen & Recovered | 964 | 2234 | 47135685 | 123398840 |
| 2448 | West Bengal | 2010 | Total Property | 7. Total Property Stolen & Recovered | 4548 | 23759 | 1168242161 | 5015168687 |
ind.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 2449 entries, 0 to 2448 Data columns (total 8 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Area_Name 2449 non-null object 1 Year 2449 non-null int64 2 Group_Name 2449 non-null object 3 Sub_Group_Name 2449 non-null object 4 Cases_Property_Recovered 2449 non-null int64 5 Cases_Property_Stolen 2449 non-null int64 6 Value_of_Property_Recovered 2449 non-null int64 7 Value_of_Property_Stolen 2449 non-null int64 dtypes: int64(5), object(3) memory usage: 153.2+ KB
ind.describe()
| Year | Cases_Property_Recovered | Cases_Property_Stolen | Value_of_Property_Recovered | Value_of_Property_Stolen | |
|---|---|---|---|---|---|
| count | 2449.000000 | 2449.000000 | 2449.000000 | 2.449000e+03 | 2.449000e+03 |
| mean | 2005.499388 | 1232.892201 | 3419.420988 | 5.859924e+07 | 2.465431e+08 |
| std | 2.873294 | 3079.573907 | 8136.256820 | 2.494403e+08 | 9.670035e+08 |
| min | 2001.000000 | 0.000000 | 0.000000 | 0.000000e+00 | 0.000000e+00 |
| 25% | 2003.000000 | 13.000000 | 45.000000 | 6.845700e+05 | 3.649018e+06 |
| 50% | 2005.000000 | 113.000000 | 358.000000 | 5.851830e+06 | 2.701800e+07 |
| 75% | 2008.000000 | 722.000000 | 1875.000000 | 3.406395e+07 | 1.214580e+08 |
| max | 2010.000000 | 27960.000000 | 80663.000000 | 7.470011e+09 | 2.377625e+10 |
ind.shape
(2449, 8)
ind.columns
Index(['Area_Name', 'Year', 'Group_Name', 'Sub_Group_Name',
'Cases_Property_Recovered', 'Cases_Property_Stolen',
'Value_of_Property_Recovered', 'Value_of_Property_Stolen'],
dtype='object')
ind = pd.read_csv("IndiaCrime.csv",index_col=["Area_Name","Value_of_Property_Stolen"])
ind.head()
| Year | Group_Name | Sub_Group_Name | Cases_Property_Recovered | Cases_Property_Stolen | Value_of_Property_Recovered | ||
|---|---|---|---|---|---|---|---|
| Area_Name | Value_of_Property_Stolen | ||||||
| Andaman & Nicobar Islands | 1321961 | 2001 | Burglary - Property | 3. Burglary | 27 | 64 | 755858 |
| Andhra Pradesh | 147019348 | 2001 | Burglary - Property | 3. Burglary | 3321 | 7134 | 51483437 |
| Arunachal Pradesh | 4931904 | 2001 | Burglary - Property | 3. Burglary | 66 | 248 | 825115 |
| Assam | 21466955 | 2001 | Burglary - Property | 3. Burglary | 539 | 2423 | 3722850 |
| Bihar | 17023937 | 2001 | Burglary - Property | 3. Burglary | 367 | 3231 | 2327135 |
ind = pd.read_csv("IndiaCrime.csv",index_col=["Sub_Group_Name","Value_of_Property_Recovered","Cases_Property_Recovered"])
ind.head()
| Area_Name | Year | Group_Name | Cases_Property_Stolen | Value_of_Property_Stolen | |||
|---|---|---|---|---|---|---|---|
| Sub_Group_Name | Value_of_Property_Recovered | Cases_Property_Recovered | |||||
| 3. Burglary | 755858 | 27 | Andaman & Nicobar Islands | 2001 | Burglary - Property | 64 | 1321961 |
| 51483437 | 3321 | Andhra Pradesh | 2001 | Burglary - Property | 7134 | 147019348 | |
| 825115 | 66 | Arunachal Pradesh | 2001 | Burglary - Property | 248 | 4931904 | |
| 3722850 | 539 | Assam | 2001 | Burglary - Property | 2423 | 21466955 | |
| 2327135 | 367 | Bihar | 2001 | Burglary - Property | 3231 | 17023937 |
ind.dropna()
| Area_Name | Year | Group_Name | Sub_Group_Name | Cases_Property_Recovered | Cases_Property_Stolen | Value_of_Property_Recovered | |
|---|---|---|---|---|---|---|---|
| Value_of_Property_Stolen | |||||||
| 1321961 | Andaman & Nicobar Islands | 2001 | Burglary - Property | 3. Burglary | 27 | 64 | 755858 |
| 147019348 | Andhra Pradesh | 2001 | Burglary - Property | 3. Burglary | 3321 | 7134 | 51483437 |
| 4931904 | Arunachal Pradesh | 2001 | Burglary - Property | 3. Burglary | 66 | 248 | 825115 |
| 21466955 | Assam | 2001 | Burglary - Property | 3. Burglary | 539 | 2423 | 3722850 |
| 17023937 | Bihar | 2001 | Burglary - Property | 3. Burglary | 367 | 3231 | 2327135 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 1317919190 | Tamil Nadu | 2010 | Total Property | 7. Total Property Stolen & Recovered | 16125 | 21509 | 660311804 |
| 33032746 | Tripura | 2010 | Total Property | 7. Total Property Stolen & Recovered | 192 | 879 | 5666102 |
| 1442670414 | Uttar Pradesh | 2010 | Total Property | 7. Total Property Stolen & Recovered | 9130 | 35068 | 577591772 |
| 123398840 | Uttarakhand | 2010 | Total Property | 7. Total Property Stolen & Recovered | 964 | 2234 | 47135685 |
| 5015168687 | West Bengal | 2010 | Total Property | 7. Total Property Stolen & Recovered | 4548 | 23759 | 1168242161 |
2449 rows × 7 columns
ind.aggregate(['sum','min'])
| Area_Name | Year | Group_Name | Cases_Property_Stolen | Value_of_Property_Stolen | |
|---|---|---|---|---|---|
| sum | Andaman & Nicobar IslandsAndhra PradeshArunach... | 4911468 | Burglary - PropertyBurglary - PropertyBurglary... | 8374162 | 603784038161 |
| min | Andaman & Nicobar Islands | 2001 | Burglary - Property | 0 | 0 |
gk = ind.groupby("Group_Name")
gk.first()
| Area_Name | Year | Cases_Property_Stolen | Value_of_Property_Stolen | |
|---|---|---|---|---|
| Group_Name | ||||
| Burglary - Property | Andaman & Nicobar Islands | 2001 | 64 | 1321961 |
| Criminal Breach of Trust - Property | Andaman & Nicobar Islands | 2001 | 10 | 1226967 |
| Dacoity -Property | Andaman & Nicobar Islands | 2001 | 0 | 0 |
| Other heads of Property | Andaman & Nicobar Islands | 2001 | 0 | 0 |
| Robbery - Property | Andaman & Nicobar Islands | 2001 | 4 | 40000 |
| Theft - Property | Andaman & Nicobar Islands | 2001 | 65 | 595549 |
| Total Property | Andaman & Nicobar Islands | 2001 | 143 | 3184477 |
url = "Iris.csv"
df = pd.read_csv(url)
df.plot()
<Axes: >
df.columns
Index(['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',
'Species'],
dtype='object')
df.plot(x="SepalLengthCm",y="SepalWidthCm")
df.plot(x="SepalLengthCm",y="SepalWidthCm",kind="scatter")
<Axes: xlabel='SepalLengthCm', ylabel='SepalWidthCm'>
df.plot(kind = 'bar',subplots=True,figsize=(8,8))
array([<Axes: title={'center': 'Id'}>,
<Axes: title={'center': 'SepalLengthCm'}>,
<Axes: title={'center': 'SepalWidthCm'}>,
<Axes: title={'center': 'PetalLengthCm'}>,
<Axes: title={'center': 'PetalWidthCm'}>], dtype=object)
df.plot(kind='bar')
<Axes: >
df.plot(kind ='barh',stacked=True)
<Axes: >
df.plot.hist()
<Axes: ylabel='Frequency'>
df.plot.area(stacked=False)
<Axes: >
ind = pd.read_csv("IndiaCrime.csv")
ind.columns
Index(['Area_Name', 'Year', 'Group_Name', 'Sub_Group_Name',
'Cases_Property_Recovered', 'Cases_Property_Stolen',
'Value_of_Property_Recovered', 'Value_of_Property_Stolen'],
dtype='object')
ind.plot(x="Cases_Property_Recovered",y="Cases_Property_Stolen")
<Axes: xlabel='Cases_Property_Recovered'>
ind.plot(x="Cases_Property_Recovered",y="Cases_Property_Stolen",kind="bar",figsize=(120,8))
<Axes: xlabel='Cases_Property_Recovered'>
ind.plot(kind="bar",figsize=(30,30),subplots=True)
array([<Axes: title={'center': 'Year'}>,
<Axes: title={'center': 'Cases_Property_Recovered'}>,
<Axes: title={'center': 'Cases_Property_Stolen'}>,
<Axes: title={'center': 'Value_of_Property_Recovered'}>,
<Axes: title={'center': 'Value_of_Property_Stolen'}>], dtype=object)
ind.plot(figsize=(8,8),subplots=True)
array([<Axes: >, <Axes: >, <Axes: >, <Axes: >, <Axes: >], dtype=object)
ind.plot.hist()
<Axes: ylabel='Frequency'>
ind.plot.area(stacked=False)
<Axes: >
ind.plot.area()
<Axes: >
ind.plot(x="Cases_Property_Recovered",y="Cases_Property_Stolen",kind="scatter")
ind.plot(x="Cases_Property_Recovered",y="Cases_Property_Stolen",kind="pie",figsize=(30,30))
<Axes: ylabel='Cases_Property_Stolen'>
ind.plot(kind ='barh',stacked=True)
<Axes: >